Klasifikasi Sinyal Elektro Encheplao Graph (EEG) menggunakan K-Nearest Neighbor (KNN)
Keywords:
Elektroensefalografi (EEG), K-Nearest Neighbor (KNN), Klasifikasi Sinyal Otak
Abstract
Judul : Klasifikasi Sinyal Elektro Encheplao Graph (EEG) menggunakan K-Nearest Neighbor (KNN)
- Bab I Pendahuluan
- Bab II Sinyal Elektro Enchepalo Graph (EEG)
- Bab III Kernel Nearest Neighbor (K-NN)
- Bab IV Ekstrasi Sinyal EEG Menggunakan Metode Fast Fourier Transform (FFT)
- Bab V Ekstrasi Sinyal EEG Menggunakan Metode Transformasi Wavelet
Editor: M.Tanzil Multazam & Mahardika Darmawan Kusuma Wardana
Published by:
Universitas Muhammadiyah Sidoarjo Press, Sidoarjo, 2024
ISBN:
- ISBN
Deskripsi :
Deskripsi ini menjelaskan tentang implementasi metode K-Nearest Neighbor (KNN) dalam klasifikasi sinyal EEG (Electroencephalography)—sinyal listrik yang dihasilkan oleh aktivitas otak manusia. Sinyal EEG memiliki sifat nonlinier, kompleks, dan rentan terhadap noise, sehingga diperlukan teknik pengolahan dan klasifikasi yang efisien untuk mengenali pola-pola aktivitas otak yang relevan, seperti pada deteksi epilepsi, tingkat konsentrasi, atau identifikasi kondisi neurologis lainnya.
Fokus Utama :
- Pengenalan dan Karakteristik Sinyal EEG
Menjelaskan sifat-sifat dasar sinyal EEG sebagai representasi aktivitas listrik otak, termasuk tantangan dalam pengolahannya seperti noise, non-stasioneritas, dan artefak. - Pra-pemrosesan Sinyal EEG
Menyajikan teknik filtering, normalisasi, dan segmentasi yang digunakan untuk membersihkan dan menyiapkan sinyal EEG sebelum ekstraksi ciri.
Fitur Unik :
- Pendekatan Sederhana namun Efektif untuk Data EEG
Menggunakan algoritma K-Nearest Neighbor (KNN) yang mudah dipahami dan diimplementasikan, namun mampu menghasilkan performa klasifikasi yang kompetitif untuk data EEG yang kompleks.
Target Audiens :
- Mahasiswa Teknik dan Sains Komputasi
Terutama dari program studi Teknik Elektro, Teknik Biomedis, Informatika, Ilmu Komputer, atau Data Science yang mempelajari pengolahan sinyal digital, kecerdasan buatan, dan machine learning.
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References
[1] J. S. Kumar and P. Bhuvaneswari, “Analysis of electroencephalography (EEG) signals and its categorization - A study,” Procedia Eng., vol. 38, pp. 2525–2536, 2012, doi: 10.1016/j.proeng.2012.06.298.
[2] M. Teplan and Institute, “Fundamentals of Eeg Measurement,” AAAI Fall Symp. - Tech. Rep., vol. FS-12-04, no. 2, pp. 59–64, 2012.
[3] Z. A. A. Alyasseri, A. T. Khader, and M. A. Al-Betar, “Electroencephalogram signals denoising using various mother wavelet functions: A comparative analysis,” ACM Int. Conf. Proceeding Ser., vol. Part F1313, no. October, pp. 100–105, 2017, doi: 10.1145/3132300.3132313.
[4] E. S. Yousif, A. S. Abdulbaqi, A. Z. Hameed, and M. N. Saif Al-Din, “Electroencephalogram Signals Classification Based on Feature Normalization,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, 2020, doi: 10.1088/1757-899X/928/3/032028.
[5] A. A. A. Meidiary, A. M. Gelgel, and I. G. N. P. Putra, “Electroencephalogram (EEG) features and clinical presentation in the elderly patient at neurologic policlinic Sanglah General Hospital between July 2015-2017 period,” Bali Med. J., vol. 8, no. 2, pp. 667–671, 2019, doi: 10.15562/bmj.v8i2.1484.
[6] N. Disorders, “The Epilepsies and Seizures”.
[7] E. Board, “Epilepsy : Report by the director - general,” Development, vol. 1, no. October, pp. 1–6, 2019, [Online]. Available: https://apps.who.int/iris/handle/10665/355987
[8] P. Fulpatil and Y. Meshram, “Analysis of EEG Signals with the Effect of Meditation,” Int. J. Eng. Res. Appl. www.ijera.com, vol. 4, no. 6, pp. 51–53, 2014, [Online]. Available: www.ijera.com
[9] M. M. Siddiqui, S. Rahman, S. H. Saeed, and A. Banodia, “EEG Signals Play Major Role to diagnose Sleep Disorder,” Int. J. Electron. Comput. Sci. Eng., vol. 503, pp. 503–505, 1956.
[10] V. Pandey, A. Agarwal, and M. M. Siddiqui, “Sleep disorders and eeg recording,” no. November, pp. 206–210, 1956.
[11] V. Menon and S. Crottaz-Herbette, “Combined EEG and fMRI Studies of Human Brain Function,” Int. Rev. Neurobiol., vol. 66, no. 05, pp. 291–321, 2005, doi: 10.1016/S0074-7742(05)66010-2.
[12] A. Melnik, W. D. Hairston, D. P. Ferris, and P. König, “EEG correlates of sensorimotor processing: Independent components involved in sensory and motor processing,” Sci. Rep., vol. 7, no. 1, 2017, doi: 10.1038/s41598-017-04757-8.
[13] A. Turnip et al., “Detection of Drug Effects on Brain Activity using EEG-P300 with Similar Stimuli,” IOP Conf. Ser. Mater. Sci. Eng., vol. 220, no. 1, 2017, doi: 10.1088/1757-899X/220/1/012042.
[14] K. L. Son et al., “Neurophysiological features of Internet gaming disorder and alcohol use disorder: A resting-state EEG study,” Transl. Psychiatry, vol. 5, no. July, pp. 2–7, 2015, doi: 10.1038/tp.2015.124.
[15] M. Balconi, S. Campanella, and R. Finocchiaro, “Web addiction in the brain: Cortical oscillations, autonomic activity, and behavioral measures,” J. Behav. Addict., vol. 6, no. 3, pp. 334–344, 2017, doi: 10.1556/2006.6.2017.041.
[16] I. Ahmad et al., “EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/6486570.
[17] A. Eviyanti, H. Hindarto, Sumarno, and H. A. A. Duddin, “Epilepsi detection system based on EEG record using neural network backpropagation method,” J. Phys. Conf. Ser., vol. 1381, no. 1, 2019, doi: 10.1088/1742-6596/1381/1/012037.
[18] S. Nefti and J. O. Gray, “Advances in cognitive systems,” Adv. Cogn. Syst., no. January, pp. 1–503, 2010, doi: 10.1049/PBCE071E.
[19] R. Thenius, P. Zahadat, and T. Schmickl, “EMANN - a model of emotions in an artificial neural network,” no. September 2013, pp. 830–837, 2013, doi: 10.7551/978-0-262-31709-2-ch122.
[20] C. K. Behera, T. K. Reddy, L. Behera, and B. Bhattacarya, “Artificial neural network based arousal detection from sleep electroencephalogram data,” I4CT 2014 - 1st Int. Conf. Comput. Commun. Control Technol. Proc., no. September, pp. 458–462, 2014, doi: 10.1109/I4CT.2014.6914226.
[21] M. Ronzhina, O. Janoušek, J. Kolářová, M. Nováková, P. Honzík, and I. Provazník, “Sleep scoring using artificial neural networks,” Sleep Med. Rev., vol. 16, no. 3, pp. 251–263, 2012, doi: 10.1016/j.smrv.2011.06.003.
[22] J. Barragán, E. F. Estrada, P. A. Nava, and H. Nazeran, “EEG-based Classification of Sleep Stages using Artificial Neural Networks,” 5th Int. Work. Biomed. Signal Interpret., pp. 0–3, 2005.
[23] A. Subasi, M. K. Kiymik, A. Alkan, and E. Koklukaya, “Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection,” Math. Comput. Appl., vol. 10, no. 1, pp. 57–70, 2005, doi: 10.3390/mca10010057.
[24] V. T. N. Nguyen, V. T. Huynh, and T. H. H. Nguyen, “Electroencephalography analysis using neural network,” NICS 2018 - Proc. 2018 5th NAFOSTED Conf. Inf. Comput. Sci., no. May 2019, pp. 144–147, 2019, doi: 10.1109/NICS.2018.8606797.
[25] I. Rakhmatulin, “Review of Neural Networks in the EEG Signal Recognition,” SSRN Electron. J., no. November, pp. 1–15, 2021, doi: 10.2139/ssrn.3765947.
[26] L. T. Car, “Hans berger ( 1873-1941 ) - The history of electroencephalography,” no. February 2005, 2016.
[27] M. Rosinova, M. Lojka, J. Stas, and J. Juhar, “Voice command recognition using EEG signals,” Proc. Elmar - Int. Symp. Electron. Mar., vol. 2017-Septe, no. September, pp. 153–156, 2017, doi: 10.23919/ELMAR.2017.8124457.
[28] K. Mohammad and S. Hamo, “Emotion Recognition and Authentication Based on Electroencephalogram ( EEG ) Signals,” pp. 1–11, 1924.
[29] C. Caloian, “Use of EEG Signal to Detect Brain Patterns Supervisor : Frank Rudzicz Related Work,” pp. 1–23, 2013.
[30] H. V. D. Parunak, S. H. Brooks, S. Brueckner, and R. Gupta, “Apoptotic stigmergic agents for real-time swarming simulation,” AAAI Fall Symp. - Tech. Rep., vol. FS-12-04, pp. 59–64, 2012.
[31] M. Zimmerman, N. A. Peterson, and M. A. Zimmerman, “Beyond the Individual : Toward a Nomological Network of Organizational Empowerment Beyond the Individual : Toward a Nomological Network of Organizational Empowerment,” vol. 34, no. October 2004, 2016, doi: 10.1023/B.
[32] S. Kotte and J. R. K. Kumar Dabbakuti, “Methods for removal of artifacts from EEG signal: A review,” J. Phys. Conf. Ser., vol. 1706, no. 1, 2020, doi: 10.1088/1742-6596/1706/1/012093.
[33] J. J. Riviello, “Pediatric EEG abnormalities,” Clin. Neurophysiol. Prim., pp. 179–204, 2007, doi: 10.1007/978-1-59745-271-7_11.
[34] U. M. D. E. C. D. E. Los, No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title.
[35] A. K. Singh and S. Krishnan, “Trends in EEG signal feature extraction applications,” Front. Artif. Intell., vol. 5, 2023, doi: 10.3389/frai.2022.1072801.
[36] K. A. I. Aboalayon, M. Faezipour, W. S. Almuhammadi, and S. Moslehpour, “Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation,” Entropy, vol. 18, no. 9, 2016, doi: 10.3390/e18090272.
[37] J. Grandgirard, D. Poinsot, L. Krespi, J. P. Nénon, and A. M. Cortesero, “Costs of secondary parasitism in the facultative hyperparasitoid Pachycrepoideus dubius: Does host size matter?,” Entomol. Exp. Appl., vol. 103, no. 3, pp. 239–248, 2002, doi: 10.1023/A.
[38] S. Wiyono and T. Abidin, “Implementation of K-Nearest Neighbour (Knn) Algorithm To Predict Student’S Performance,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 2, pp. 873–878, 2018, doi: 10.24176/simet.v9i2.2424.
[39] P. Cunningham and S. J. Delany, “K -Nearest Neighbour Classifiers,” Mult. Classif. Syst., pp. 1–17, 2007.
[40] A. Tversky, “Features of Similarity,” Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence. pp. 290–302, 2013. doi: 10.1016/B978-1-4832-1446-7.50025-X.
[41] I. M. K. Karo, A. Khosuri, and R. Setiawan, “Effects of Distance Measurement Methods in K-Nearest Neighbor Algorithm to Select Indonesia Smart Card Recipient,” 2021 Int. Conf. Data Sci. Its Appl. ICoDSA 2021, no. October, pp. 209–214, 2021, doi: 10.1109/ICoDSA53588.2021.9617476.
[42] S. V. Patil and D. B. Kulkarni, “A review of dimensionality reduction in high-dimensional data using multi-core and many-core architecture,” Commun. Comput. Inf. Sci., vol. 964, pp. 54–63, 2019, doi: 10.1007/978-981-13-7729-7_4.
[43] S. Li, K. Zhang, Q. Chen, S. Wang, and S. Zhang, “Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm,” IEEE Access, vol. 8, pp. 139512–139528, 2020, doi: 10.1109/ACCESS.2020.3012768.
[44] B. Smyth, E. Mckenna, M. A. Ferrario, P. Cunningham, D. Mcsherry, and D. Leake, ““ There ’ s a hole in my case-base !”,” October, no. October 2003, pp. 1–25, 2014, doi: 10.1007/3-540-44527-7.
[45] Z. Isik and L. O. U. Gripentoft, “EEG Signal Analysis in the Frequency Domain An Examination of Abnormalities During the Gait Cycle,” 2022.
[46] K. S. Bayram, M. A. Kizrak, and B. Bolat, “Classification of EEG signals by using support vector machines,” 2013 IEEE Int. Symp. Innov. Intell. Syst. Appl. IEEE INISTA 2013, no. June, 2013, doi: 10.1109/INISTA.2013.6577636.
[47] P. Zhong, D. Wang, and C. Miao, “EEG-Based Emotion Recognition Using Regularized Graph Neural Networks,” IEEE Trans. Affect. Comput., vol. 13, no. 3, pp. 1290–1301, 2022, doi: 10.1109/TAFFC.2020.2994159.
[48] V. P. Oikonomou, K. Georgiadis, G. Liaros, S. Nikolopoulos, and I. Kompatsiaris, “A Comparison Study on EEG Signal Processing Techniques Using Motor Imagery EEG Data,” Proc. - IEEE Symp. Comput. Med. Syst., vol. 2017-June, no. November, pp. 781–786, 2017, doi: 10.1109/CBMS.2017.113.
[49] J. F. Hwaidi and T. M. Chen, “Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach,” IEEE Access, vol. 10, no. Mi, pp. 48071–48081, 2022, doi: 10.1109/ACCESS.2022.3171906.
[50] H. Hindarto and S. Sumarno, “Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform,” CommIT (Communication Inf. Technol. J., vol. 10, no. 2, p. 49, 2016, doi: 10.21512/commit.v10i2.1548.
[51] T. H. Shovon, Z. Al Nazi, S. Dash, and M. F. Hossain, “Classification of motor imagery EEG signals with multi-input convolutional neural network by augmenting STFT,” 2019 5th Int. Conf. Adv. Electr. Eng. ICAEE 2019, no. August, pp. 398–403, 2019, doi: 10.1109/ICAEE48663.2019.8975578.
[52] N. E. Binti Md Isa, A. Amir, M. Z. Ilyas, and M. S. Razalli, “Motor imagery classification in brain computer interface (BCI) based on EEG signal by using machine learning technique,” Bull. Electr. Eng. Informatics, vol. 8, no. 1, pp. 269–275, 2019, doi: 10.11591/eei.v8i1.1402.
[53] M. Rafiqul Islam, “Wavelets, its Application and Technique in signal and image processing,” Glob. J. Comput. Sci. Technol., vol. 11, no. 1–13, 2011.
[54] A. Augustine, R. D. Prakash, and R. Xavier, “Detection and Classification of three phase Power Quality events using Wavelets Transforms and Soft Computing Techniques,” vol. 4, no. 1, pp. 32–38, 2016.
[55] T. A. Salih and Y. M. Abdal, “Brain computer interface based smart keyboard using neurosky mindwave headset,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 2, pp. 919–927, 2020, doi: 10.12928/TELKOMNIKA.V18I2.13993.
[56] Okfalisa, I. Gazalba, Mustakim, and N. G. I. Reza, “Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification,” Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-January, pp. 294–298, 2018, doi: 10.1109/ICITISEE.2017.8285514.
[57] H. Taherdoost, “Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research,” SSRN Electron. J., no. September, 2018, doi: 10.2139/ssrn.3205035.
[58] A. S. Al-Fahoum and A. A. Al-Fraihat, “Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains,” ISRN Neurosci., vol. 2014, no. February 2014, pp. 1–7, 2014, doi: 10.1155/2014/730218.
[59] B. Blankertz et al., “The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1044–1051, 2004, doi: 10.1109/TBME.2004.826692.
[60] A. Bablani, D. R. Edla, and S. Dodia, “Classification of EEG data using k-nearest neighbor approach for concealed information test,” Procedia Comput. Sci., vol. 143, pp. 242–249, 2018, doi: 10.1016/j.procs.2018.10.392.
[2] M. Teplan and Institute, “Fundamentals of Eeg Measurement,” AAAI Fall Symp. - Tech. Rep., vol. FS-12-04, no. 2, pp. 59–64, 2012.
[3] Z. A. A. Alyasseri, A. T. Khader, and M. A. Al-Betar, “Electroencephalogram signals denoising using various mother wavelet functions: A comparative analysis,” ACM Int. Conf. Proceeding Ser., vol. Part F1313, no. October, pp. 100–105, 2017, doi: 10.1145/3132300.3132313.
[4] E. S. Yousif, A. S. Abdulbaqi, A. Z. Hameed, and M. N. Saif Al-Din, “Electroencephalogram Signals Classification Based on Feature Normalization,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, 2020, doi: 10.1088/1757-899X/928/3/032028.
[5] A. A. A. Meidiary, A. M. Gelgel, and I. G. N. P. Putra, “Electroencephalogram (EEG) features and clinical presentation in the elderly patient at neurologic policlinic Sanglah General Hospital between July 2015-2017 period,” Bali Med. J., vol. 8, no. 2, pp. 667–671, 2019, doi: 10.15562/bmj.v8i2.1484.
[6] N. Disorders, “The Epilepsies and Seizures”.
[7] E. Board, “Epilepsy : Report by the director - general,” Development, vol. 1, no. October, pp. 1–6, 2019, [Online]. Available: https://apps.who.int/iris/handle/10665/355987
[8] P. Fulpatil and Y. Meshram, “Analysis of EEG Signals with the Effect of Meditation,” Int. J. Eng. Res. Appl. www.ijera.com, vol. 4, no. 6, pp. 51–53, 2014, [Online]. Available: www.ijera.com
[9] M. M. Siddiqui, S. Rahman, S. H. Saeed, and A. Banodia, “EEG Signals Play Major Role to diagnose Sleep Disorder,” Int. J. Electron. Comput. Sci. Eng., vol. 503, pp. 503–505, 1956.
[10] V. Pandey, A. Agarwal, and M. M. Siddiqui, “Sleep disorders and eeg recording,” no. November, pp. 206–210, 1956.
[11] V. Menon and S. Crottaz-Herbette, “Combined EEG and fMRI Studies of Human Brain Function,” Int. Rev. Neurobiol., vol. 66, no. 05, pp. 291–321, 2005, doi: 10.1016/S0074-7742(05)66010-2.
[12] A. Melnik, W. D. Hairston, D. P. Ferris, and P. König, “EEG correlates of sensorimotor processing: Independent components involved in sensory and motor processing,” Sci. Rep., vol. 7, no. 1, 2017, doi: 10.1038/s41598-017-04757-8.
[13] A. Turnip et al., “Detection of Drug Effects on Brain Activity using EEG-P300 with Similar Stimuli,” IOP Conf. Ser. Mater. Sci. Eng., vol. 220, no. 1, 2017, doi: 10.1088/1757-899X/220/1/012042.
[14] K. L. Son et al., “Neurophysiological features of Internet gaming disorder and alcohol use disorder: A resting-state EEG study,” Transl. Psychiatry, vol. 5, no. July, pp. 2–7, 2015, doi: 10.1038/tp.2015.124.
[15] M. Balconi, S. Campanella, and R. Finocchiaro, “Web addiction in the brain: Cortical oscillations, autonomic activity, and behavioral measures,” J. Behav. Addict., vol. 6, no. 3, pp. 334–344, 2017, doi: 10.1556/2006.6.2017.041.
[16] I. Ahmad et al., “EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/6486570.
[17] A. Eviyanti, H. Hindarto, Sumarno, and H. A. A. Duddin, “Epilepsi detection system based on EEG record using neural network backpropagation method,” J. Phys. Conf. Ser., vol. 1381, no. 1, 2019, doi: 10.1088/1742-6596/1381/1/012037.
[18] S. Nefti and J. O. Gray, “Advances in cognitive systems,” Adv. Cogn. Syst., no. January, pp. 1–503, 2010, doi: 10.1049/PBCE071E.
[19] R. Thenius, P. Zahadat, and T. Schmickl, “EMANN - a model of emotions in an artificial neural network,” no. September 2013, pp. 830–837, 2013, doi: 10.7551/978-0-262-31709-2-ch122.
[20] C. K. Behera, T. K. Reddy, L. Behera, and B. Bhattacarya, “Artificial neural network based arousal detection from sleep electroencephalogram data,” I4CT 2014 - 1st Int. Conf. Comput. Commun. Control Technol. Proc., no. September, pp. 458–462, 2014, doi: 10.1109/I4CT.2014.6914226.
[21] M. Ronzhina, O. Janoušek, J. Kolářová, M. Nováková, P. Honzík, and I. Provazník, “Sleep scoring using artificial neural networks,” Sleep Med. Rev., vol. 16, no. 3, pp. 251–263, 2012, doi: 10.1016/j.smrv.2011.06.003.
[22] J. Barragán, E. F. Estrada, P. A. Nava, and H. Nazeran, “EEG-based Classification of Sleep Stages using Artificial Neural Networks,” 5th Int. Work. Biomed. Signal Interpret., pp. 0–3, 2005.
[23] A. Subasi, M. K. Kiymik, A. Alkan, and E. Koklukaya, “Neural network classification of EEG signals by using AR with MLE preprocessing for epileptic seizure detection,” Math. Comput. Appl., vol. 10, no. 1, pp. 57–70, 2005, doi: 10.3390/mca10010057.
[24] V. T. N. Nguyen, V. T. Huynh, and T. H. H. Nguyen, “Electroencephalography analysis using neural network,” NICS 2018 - Proc. 2018 5th NAFOSTED Conf. Inf. Comput. Sci., no. May 2019, pp. 144–147, 2019, doi: 10.1109/NICS.2018.8606797.
[25] I. Rakhmatulin, “Review of Neural Networks in the EEG Signal Recognition,” SSRN Electron. J., no. November, pp. 1–15, 2021, doi: 10.2139/ssrn.3765947.
[26] L. T. Car, “Hans berger ( 1873-1941 ) - The history of electroencephalography,” no. February 2005, 2016.
[27] M. Rosinova, M. Lojka, J. Stas, and J. Juhar, “Voice command recognition using EEG signals,” Proc. Elmar - Int. Symp. Electron. Mar., vol. 2017-Septe, no. September, pp. 153–156, 2017, doi: 10.23919/ELMAR.2017.8124457.
[28] K. Mohammad and S. Hamo, “Emotion Recognition and Authentication Based on Electroencephalogram ( EEG ) Signals,” pp. 1–11, 1924.
[29] C. Caloian, “Use of EEG Signal to Detect Brain Patterns Supervisor : Frank Rudzicz Related Work,” pp. 1–23, 2013.
[30] H. V. D. Parunak, S. H. Brooks, S. Brueckner, and R. Gupta, “Apoptotic stigmergic agents for real-time swarming simulation,” AAAI Fall Symp. - Tech. Rep., vol. FS-12-04, pp. 59–64, 2012.
[31] M. Zimmerman, N. A. Peterson, and M. A. Zimmerman, “Beyond the Individual : Toward a Nomological Network of Organizational Empowerment Beyond the Individual : Toward a Nomological Network of Organizational Empowerment,” vol. 34, no. October 2004, 2016, doi: 10.1023/B.
[32] S. Kotte and J. R. K. Kumar Dabbakuti, “Methods for removal of artifacts from EEG signal: A review,” J. Phys. Conf. Ser., vol. 1706, no. 1, 2020, doi: 10.1088/1742-6596/1706/1/012093.
[33] J. J. Riviello, “Pediatric EEG abnormalities,” Clin. Neurophysiol. Prim., pp. 179–204, 2007, doi: 10.1007/978-1-59745-271-7_11.
[34] U. M. D. E. C. D. E. Los, No 主観的健康感を中心とした在宅高齢者における 健康関連指標に関する共分散構造分析Title.
[35] A. K. Singh and S. Krishnan, “Trends in EEG signal feature extraction applications,” Front. Artif. Intell., vol. 5, 2023, doi: 10.3389/frai.2022.1072801.
[36] K. A. I. Aboalayon, M. Faezipour, W. S. Almuhammadi, and S. Moslehpour, “Sleep stage classification using EEG signal analysis: A comprehensive survey and new investigation,” Entropy, vol. 18, no. 9, 2016, doi: 10.3390/e18090272.
[37] J. Grandgirard, D. Poinsot, L. Krespi, J. P. Nénon, and A. M. Cortesero, “Costs of secondary parasitism in the facultative hyperparasitoid Pachycrepoideus dubius: Does host size matter?,” Entomol. Exp. Appl., vol. 103, no. 3, pp. 239–248, 2002, doi: 10.1023/A.
[38] S. Wiyono and T. Abidin, “Implementation of K-Nearest Neighbour (Knn) Algorithm To Predict Student’S Performance,” Simetris J. Tek. Mesin, Elektro dan Ilmu Komput., vol. 9, no. 2, pp. 873–878, 2018, doi: 10.24176/simet.v9i2.2424.
[39] P. Cunningham and S. J. Delany, “K -Nearest Neighbour Classifiers,” Mult. Classif. Syst., pp. 1–17, 2007.
[40] A. Tversky, “Features of Similarity,” Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence. pp. 290–302, 2013. doi: 10.1016/B978-1-4832-1446-7.50025-X.
[41] I. M. K. Karo, A. Khosuri, and R. Setiawan, “Effects of Distance Measurement Methods in K-Nearest Neighbor Algorithm to Select Indonesia Smart Card Recipient,” 2021 Int. Conf. Data Sci. Its Appl. ICoDSA 2021, no. October, pp. 209–214, 2021, doi: 10.1109/ICoDSA53588.2021.9617476.
[42] S. V. Patil and D. B. Kulkarni, “A review of dimensionality reduction in high-dimensional data using multi-core and many-core architecture,” Commun. Comput. Inf. Sci., vol. 964, pp. 54–63, 2019, doi: 10.1007/978-981-13-7729-7_4.
[43] S. Li, K. Zhang, Q. Chen, S. Wang, and S. Zhang, “Feature Selection for High Dimensional Data Using Weighted K-Nearest Neighbors and Genetic Algorithm,” IEEE Access, vol. 8, pp. 139512–139528, 2020, doi: 10.1109/ACCESS.2020.3012768.
[44] B. Smyth, E. Mckenna, M. A. Ferrario, P. Cunningham, D. Mcsherry, and D. Leake, ““ There ’ s a hole in my case-base !”,” October, no. October 2003, pp. 1–25, 2014, doi: 10.1007/3-540-44527-7.
[45] Z. Isik and L. O. U. Gripentoft, “EEG Signal Analysis in the Frequency Domain An Examination of Abnormalities During the Gait Cycle,” 2022.
[46] K. S. Bayram, M. A. Kizrak, and B. Bolat, “Classification of EEG signals by using support vector machines,” 2013 IEEE Int. Symp. Innov. Intell. Syst. Appl. IEEE INISTA 2013, no. June, 2013, doi: 10.1109/INISTA.2013.6577636.
[47] P. Zhong, D. Wang, and C. Miao, “EEG-Based Emotion Recognition Using Regularized Graph Neural Networks,” IEEE Trans. Affect. Comput., vol. 13, no. 3, pp. 1290–1301, 2022, doi: 10.1109/TAFFC.2020.2994159.
[48] V. P. Oikonomou, K. Georgiadis, G. Liaros, S. Nikolopoulos, and I. Kompatsiaris, “A Comparison Study on EEG Signal Processing Techniques Using Motor Imagery EEG Data,” Proc. - IEEE Symp. Comput. Med. Syst., vol. 2017-June, no. November, pp. 781–786, 2017, doi: 10.1109/CBMS.2017.113.
[49] J. F. Hwaidi and T. M. Chen, “Classification of Motor Imagery EEG Signals Based on Deep Autoencoder and Convolutional Neural Network Approach,” IEEE Access, vol. 10, no. Mi, pp. 48071–48081, 2022, doi: 10.1109/ACCESS.2022.3171906.
[50] H. Hindarto and S. Sumarno, “Feature Extraction of Electroencephalography Signals Using Fast Fourier Transform,” CommIT (Communication Inf. Technol. J., vol. 10, no. 2, p. 49, 2016, doi: 10.21512/commit.v10i2.1548.
[51] T. H. Shovon, Z. Al Nazi, S. Dash, and M. F. Hossain, “Classification of motor imagery EEG signals with multi-input convolutional neural network by augmenting STFT,” 2019 5th Int. Conf. Adv. Electr. Eng. ICAEE 2019, no. August, pp. 398–403, 2019, doi: 10.1109/ICAEE48663.2019.8975578.
[52] N. E. Binti Md Isa, A. Amir, M. Z. Ilyas, and M. S. Razalli, “Motor imagery classification in brain computer interface (BCI) based on EEG signal by using machine learning technique,” Bull. Electr. Eng. Informatics, vol. 8, no. 1, pp. 269–275, 2019, doi: 10.11591/eei.v8i1.1402.
[53] M. Rafiqul Islam, “Wavelets, its Application and Technique in signal and image processing,” Glob. J. Comput. Sci. Technol., vol. 11, no. 1–13, 2011.
[54] A. Augustine, R. D. Prakash, and R. Xavier, “Detection and Classification of three phase Power Quality events using Wavelets Transforms and Soft Computing Techniques,” vol. 4, no. 1, pp. 32–38, 2016.
[55] T. A. Salih and Y. M. Abdal, “Brain computer interface based smart keyboard using neurosky mindwave headset,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 18, no. 2, pp. 919–927, 2020, doi: 10.12928/TELKOMNIKA.V18I2.13993.
[56] Okfalisa, I. Gazalba, Mustakim, and N. G. I. Reza, “Comparative analysis of k-nearest neighbor and modified k-nearest neighbor algorithm for data classification,” Proc. - 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. ICITISEE 2017, vol. 2018-January, pp. 294–298, 2018, doi: 10.1109/ICITISEE.2017.8285514.
[57] H. Taherdoost, “Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research,” SSRN Electron. J., no. September, 2018, doi: 10.2139/ssrn.3205035.
[58] A. S. Al-Fahoum and A. A. Al-Fraihat, “Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains,” ISRN Neurosci., vol. 2014, no. February 2014, pp. 1–7, 2014, doi: 10.1155/2014/730218.
[59] B. Blankertz et al., “The BCI competition 2003: Progress and perspectives in detection and discrimination of EEG single trials,” IEEE Trans. Biomed. Eng., vol. 51, no. 6, pp. 1044–1051, 2004, doi: 10.1109/TBME.2004.826692.
[60] A. Bablani, D. R. Edla, and S. Dodia, “Classification of EEG data using k-nearest neighbor approach for concealed information test,” Procedia Comput. Sci., vol. 143, pp. 242–249, 2018, doi: 10.1016/j.procs.2018.10.392.
Published
2025-05-05
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Hindarto, H. (2025). Klasifikasi Sinyal Elektro Encheplao Graph (EEG) menggunakan K-Nearest Neighbor (KNN). Umsida Press, 1 - 54. Retrieved from https://press.umsida.ac.id/index.php/umsidapress/article/view/1511
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